Population-based socio-demographic household assessment of livelihoods and health among communities in Migori County, Kenya over multiple timepoints (2021, 2024, 2027): A study protocol.

Migori County is located in western Kenya bordering Lake Victoria and has traditionally performed poorly on important health metrics, including child mortality and HIV prevalence. The Lwala Community Alliance is a non-governmental organization that serves to promote the health and well-being of communities in Migori County through an innovative model utilizing community health workers, community committees, and high-quality facility-based care. This has led to improved outcomes in areas served, including improvements in childhood mortality. As the Lwala Community Alliance expands to new programming areas, it has partnered with multiple academic institutions to rigorously evaluate outcomes. We describe a repeated cross-sectional survey study to evaluate key health metrics in both areas served by the Lwala Community Alliance and comparison areas. This will allow for longitudinal evaluation of changes in metrics over time. Surveys will be administered by trained enumerators on a tablet-based platform to maintain high data quality.

Introduction

Migori County is located in western Kenya bordering Lake Victoria (Fig 1). One of the 47 counties in Kenya, Migori has historically underperformed on many important health metrics. For example, the under-five mortality rate in 2014 was 82 per 1,000 live births compared with 52 per 1,000 live births for Kenya as a whole [1]. HIV prevalence as of 2018 was 13% in Migori County compared to 4.9% nationally [2]. Per the 2014 Kenya Demographic Health Survey (DHS), only 57.2% of children in Migori County were fully vaccinated compared with 74.9% for Kenya as a whole [1]. Importantly, most data on these health indices come from national surveys such as the DHS and are disaggregated only to the regional or county level. Hyper-local data to inform programming efforts in smaller areas is frequently not available.

Fig 1

Migori County, Kenya.

Lwala programming began in North Kamagambo in Rongo sub-county (green). By 2021, programming will include all of Rongo sub-county. The next expansion is planned for Awendo (orange). Two areas in Uriri, Central Kanyamkago and West Kanyamkago, serve as comparison wards (red).

The Lwala Community Alliance (Lwala) is a non-governmental organization that serves to promote the health and well-being of communities in the Rongo sub-county of Migori County, Kenya (Fig 2). Founded in 2007, Lwala initially worked in the community of North Kamagambo ward of Rongo sub-county with services including the operation of a hospital and clinic with inpatient, outpatient, maternal, and HIV care, as well as an innovative Community Health Worker (CHW) program incorporating traditional birth attendants. The CHW program is distinguished by its consistent payment, supportive supervision, and proactive community case finding and case management. This community health worker structure is supported by community committees that plan health initiatives and advocate for child rights, reproductive rights, sanitation infrastructure, and reduced HIV stigma.

Fig 2

Lwala Community Alliance theory of change.

Lwala utilizes a community-led model to improve health, education, and economic outcomes.

Lwala’s efforts in North Kamagambo have led to significant successes in several key health metrics, including being on track to outpace Millennium and Sustainable Development Goal (SDG) targets for childhood mortality, attaining an under-five mortality rate of 29.5 per 1,000 live births as of 2017 [3] compared to the SDG target of under 25 deaths per 1,000 live births by 2030 [4]. This success has led to local health authorities inviting Lwala to expand its CHW model into nearby East and South Kamagambo wards, as well as to begin providing technical assistance toward improved health service delivery in the government-supported health facilities of these wards. Along with service expansion has come the effort to systematically and academically evaluate health metrics and changes in outcomes in the community. This led to multiple iterations of a community-wide household survey in Lwala’s original catchment area and subsequently in the expansion areas [3,5,6]. We now aim to conduct repeated cross-sectional surveys to evaluate the effect of Lwala’s programming over time and characterize health metrics in the area.

Here we describe the Lwala Household Survey (LHS) with the following objectives: (1) to assess the health, socioeconomic, and education status of current and future communities receiving programming from Lwala; (2) to measure changes in these metrics over time in the presence of Lwala programming; (3) to compare changes over time in key metrics to determine the effect of Lwala presence and programming.

Methods

Study design

This is a repeated, cross-sectional survey allowing for longitudinal analyses of population and community-level metrics related to health, education, and socioeconomics. Households will be selected for surveying in areas currently receiving Lwala programming and in areas planned to receive Lwala programming in the future. Nearby geographic regions with no planned Lwala services will be surveyed and serve as comparison locations. Subsequent surveys will provide new cross-sectional data for a given geographic area by randomly selecting households for interview utilizing the same sampling strategy; however, it will not specifically target the same households for future surveying. Data collection will begin in 2021 and occur every three years until 2027. Previous surveys have been conducted in 2017 and 2019.

Study population, setting, and timeline

Located in the Lake Victoria region of southwestern Kenya, Migori county had a population of 1.1 million people during the 2019 National Census [7]. Administratively, there are 10 sub-counties, each with multiple smaller electoral sub-divisions called wards [8]. With an average household size of 4.6 and a growth rate of 3.1%, it is highly densely populated (427 persons per square kilometer) [7,8]. About 90% of the population live in rural areas, largely in mud-walled houses with agriculture and fishing as the main livelihoods [8].

Lwala programming began in North Kamagambo, an area covering 46.4 square kilometers, in Rongo, which is one of the 10 sub-counties, in 2007 (Fig 1). Programs were subsequently expanded to other wards in Rongo, namely East Kamagambo in 2018 and South Kamagambo in 2019. Programming will be expanded to Central Kamagambo within Rongo in 2021 following the first survey administration. Lwala’s area of programming is expanding approximately every two years, with the next round of expansions planned for Awendo sub-county. The first iteration of this survey in 2021 will be administered in current programming wards in Rongo sub-county (North Kamagambo, East Kamagambo, South Kamagambo) and the future programming ward in Rongo sub-county (Central Kamagambo). It will also include two representative wards in Awendo sub-county (North Sakwa and Central Sakwa).

The initial survey will also include two comparison wards in Uriri sub-county (Central Kanyamkago and West Kanyamkago). Uriri sub-county is adjacent enough to be a comparable location but distant enough to minimize spillover effects. In addition, Uriri sub-county has a similar socio-economic and demographic context that is analogous to Rongo sub-County. Within Uriri sub-County, Central Kanyamkago was selected as a peri-urban ward to serve as a comparison with the peri-urban Central Kamagambo. Similarly, West Kanyamkago was selected as a rural ward to compare with more rural programming wards. While Uriri has government health facilities that are typical of Migori County, there is no similar organization to Lwala.

All subsequent surveys (2024 and 2027) will include the areas from the initial 2021 survey (Table 1). Any further expansion areas that are identified will also be included. Inclusion of new wards will be approved by the investigators with subsequent amendments made to Institutional Review Board protocols.

Table 1

Survey timepoints across areas.

Sub-County	Ward	Intervention	2017*	2019*	2021	2024	2027
Rongo	North Kamagambo	2007	X	X	X	X	X
	East Kamagambo	2018		X	X	X	X
	South Kamagambo	2019		X	X	X	X
	Central Kamagambo	2021		X	X	X	X
Awendo	North Sakwa	2022			X	X	X
	Central Sakwa	2022			X	X	X
Uriri	Central Kanyamkago	Comparison		X	X	X	X
	West Kanyamkago	Comparison		X	X	X	X

*Surveys conducted as a part of previous works.

Sample size

The study has a wide range of indicators of interest, including child mortality, skilled delivery rate, vaccination coverage, contraceptive prevalence, and antenatal care. These metrics vary in their community prevalence and would thus require different sample sizes. For example, under-five mortality is relatively rare at 82 per 1,000 live births while full vaccination is relatively common at 57.2% of children [1]. A community prevalence of 50%, yielding maximum variation and therefore maximum sample size, was used to adequately power all metrics. Within each area, the sample size was calculated to detect a 10% difference over time using a power of 80%, precision of 0.05, and design effect of 1.6. Design effect was calculated according to the equation: where DE is the design effect, m is the number of the household to be sampled per cluster, and ICC is the inter-cluster correlation. An ICC of 0.15 was used based on international standards [9]. This would require a sample of 621 households in each area. This estimate was inflated by 30% to give a total goal sample of 887 per area. For the first survey, which includes eight areas, the total sample size will be 7,096 households. Subsequent surveys will include the same number of households per area. Table 2 below shows the sample size for each ward.

Table 2

Sample sizes for successive surveys.

	Minimum	Maximum	Number of clusters	Households with children	Additional Households	Total per Cluster
North Kamagambo	621	887	127	5	2	7
East Kamagambo	621	887	127	5	2	7
Central Kamagambo	621	887	127	5	2	7
South Kamagambo	621	887	127	5	2	7
North Sakwa	621	887	127	5	2	7
Central Sakwa	621	887	127	5	2	7
Central Kanyamkago	621	887	127	5	2	7
West Kanyamkago	621	887	127	5	2	7

Sampling strategy

Households will be selected using a hybrid sampling technique to obtain as random of a sample as is feasible. Because a truly random sample would be logistically infeasible due to expense and lack of a household-level sampling frame, a hybrid systematic and random sampling technique will be used. To accomplish this, a modified procedure based on the World Health Organization Expanded Programme of Immunization (EPI) method will be used [10,11]. First, each area will be split into 127 grid squares using Geographic Information System (GIS) technology. The center point of each grid cell will then be generated using GIS. This is the starting location for the enumerators for each day’s survey. GPS will be used to navigate to the precise starting location each morning.

After arrival at the center point, the spin-the-bottle technique will be used [10]. Each enumerator team will be supplied with a random direction by spinning a pen or bottle. On arrival to the center of the grid cell, they will travel in the given direction surveying houses along the line given by the direction.

As many of Lwala’s programs and outcomes of interest focus around maternal and child health, households with children under five years of age will be oversampled. At least five of seven surveys administered in each grid square will be administered to households with children under five years. If the enumerator reaches the end of the grid cell before surveying both seven total households and five households with a child under five years, they will walk along the edge of the grid to the closest corner to find more households.

This approach minimizes the biases of the traditional spin-the-bottle sampling method [12] by using the center of an arbitrary square in place of the center of a town or gathering area.

Survey instrument

The survey tool contains over 300 questions and is based on several different validated tools (Table 3). The full survey is available in the supplemental materials (S1 Appendix). The survey is designed to capture metrics across 13 public health modules in a reproducible manner. The estimated time to complete one interview is 45 minutes.

Table 3

Survey modules, key metrics, and sample sizes.

Survey Section	Question Sources	Key Metrics
Personal Demographics	Kenya Demographic and Health Survey [1]	AgeMarital status
Household Information	Kenya Demographic and Health Survey [1]	Household censusBirth history (child mortality)Total fertility rate
Economics	Kenya Demographic and Health Survey [1]Poverty Probability Index [13]	Poverty probabilityMultidimensional poverty indexIncome
Family Planning	Kenya Demographic and Health Survey [1]Condom Use Self-Efficacy Scale [14]	Contraceptive prevalence rateUnmet need for contraception
Child Health	Kenya Demographic and Health Survey [1]	Antenatal care visitsCareseeking for childhood illness
Nutrition	WHO Infant and Young Child Feeding [15]Household Hunger Scale [16]	Ever breastfedExclusive breastfeedingMinimum acceptable dietHousehold hunger
Vaccinations	Kenya Demographic and Health Survey [1]	Complete vaccination rate
HIV	Kenya Demographic and Health Survey [1]van Rie Stigma Scale [17–19]	HIV testing rateHIV stigma
Water and Sanitation	Kenya Demographic and Health Survey [1]	Drinking water sourcePrevious sanitation training
Education	Kenya Demographic and Health Survey [1]	School attendanceEducational attainment
Interpersonal Violence	Kenya Demographic and Health Survey [1]Abuse Assessment Screen [20]Partner Violence Screen [21]	Interpersonal violence prevalence
Mental Health	Patient Health Questionnaire (PHQ-8) [22,23]	Depressive symptom prevalence
Programming	Lwala monitoring and evaluation tools	Service accessService satisfaction
Observational	Kenya Demographic and Health Survey [1]	Mosquito net useHandwashing facilityLatrine type
COVID-19	WHO COVID Survey Tool and Guidance [24]van Rie Stigma Scale (adapted) [17–19]	Personal COVID-19 experiencePrevention behaviorsTesting and vaccination perceptionsCOVID-19 stigma

Participant recruitment and enrollment procedures

Upon arrival to a selected household, the enumerator will first ask to speak to the head of the household. If the head of household is present, the enumerator will then ask if they have children under 5 years old living in the household. If no head of household is present, the enumerator will skip this house, going to the next household along the line selected. They will return to the household later if the head of household is returning home. We define a household as a group of people that eat under the same roof that have lived in the same dwelling for the past year. This excludes temporary visitors.

Heads of households that are 18 years of age or older will be surveyed. Female heads of household are preferred as female family planning, interpersonal violence, and child health and nutrition are key survey areas. Male heads of household will only be surveyed if female heads are unavailable. The only other inclusion criteria will be living in a household in one of the surveyed communities. Households with children under five years of age will be oversampled. Participants will receive 50 KES (about $0.50) in airtime for their participation.

Ethical considerations

The protocol and study design for our household survey was approved by the Ethics and Scientific Review Committee at AMREF Health Africa on March 29, 2021 (AMREF-ESRC P452/2018) and the Institutional Review Board at Northeastern University on September 21, 2020 (IRB #: 20-09-18). A research permit was obtained through the National Commission for Science, Technology and Innovation in Kenya on February 11, 2021 (NACOSTI/P/21/8776). All study personnel will undergo ethical research training.

Safety and privacy

Prior to data collection, enumerators will obtain informed consent from each respondent in the form of a signature or thumbprint after reading a standardized script informing the respondent of the survey’s purpose and confidentiality policy. Respondents who cannot read or write will be requested to invite a witness to participate in the consenting process. Potential participants are then encouraged to ask questions about the household survey before signing the consent form. Minors (below age 18) will not be surveyed, and consent will only be obtained from adults. For sensitive survey questions, specifically questions regarding mental health and interpersonal violence, an additional female enumerator will be available if female respondents prefer. Respondents will also be provided a contact number for a mental health counselor if concerns are identified.

Data from survey responses will primarily exist as digital copies. If paper surveys are administered due to technology failure, they will be entered into the electronic tool as soon as possible. Paper surveys will be kept in a locked, secure area. Data will be temporarily stored on individual tablets that are password-protected. Upon completion of the survey, data will be uploaded to a secure, privacy-protected online server. Enumerators are required to sign a form declaring that they will keep information obtained confidential and will undergo privacy training prior to survey administration. Interviews will be conducted in as private a location as available in the setting to avoid breaching respondent privacy during the survey itself.

The survey in 2021 will be conducted in the ongoing context of the COVID-19 pandemic. Standard operating procedures have been established to maximally diminish the risk of transmission and to protect both household participants and enumerators. Enumerators will be trained on transmission and prevention of COVID-19. All enumerators will be tested at the beginning of training, prior to survey implementation, and every two weeks during survey administration using a rapid diagnostic test (RDT). Enumerators will be screened for COVID-19-related symptoms each day [25], and any enumerator with symptoms will be referred for testing. If an enumerator tests positive, the Ministry of Health will be notified according to national guidelines [26]. Data collection teams and participants will be provided with sanitation materials and face masks. At all times, social distancing will be maintained between enumerators and participants. In addition, because most people spend their day outdoors and the survey takes place during daylight, interviews will be conducted primarily in outdoor settings to minimize risk of exposure. Each selected potential participant will be asked a series of COVID-19 exposure and symptom questions before the survey can begin. If there is concern for COVID-19 infection, this respondent will not be surveyed. At the end of the survey period enumerators will also be tested for COVID-19 using an RDT to assure no infection occurred during the survey. Lwala will conduct follow-up response per Ministry of Health guidelines, including notifying potentially exposed respondents.

Enumerator selection, training, and team composition

Surveys will be administered in the household by trained enumerators who are not regular Lwala staff. All enumerators will be hired from the community. Enumerators will be selected from a pool of college graduates or equivalent experience, with preference given to those with experience in survey administration, Dholuo fluency, and high performance in training.

Prior to survey implementation, the enumerators will participate in a five-day training intended to familiarize them with the survey questions and tablet platform, the methodology for household and respondent selection, and recommendations for dealing with potential challenges in the field. Training will focus on the responsible conduct of research, an understanding of the intent of the survey questions, the appropriate translation options in Dholuo, the appropriate skipping of questions according to survey logic, and the procedure for marking responses based on the question type. Enumerators that show signs that indicate the inability to interact appropriately with interviewees or generally do not perform well will be dismissed before field data collection begins.

Data collection teams will consist of a team leader and two enumerators. The team leader will assist in household identification and survey consent while enumerators are completing surveys with eligible households. An overall survey supervisor will also be present to assist with problems that arise and conduct spot checks by observing surveys.

Data collection, management, and quality assurance

Enumerators will enter data on tablet-based questionnaires created using Research Electronic Data Capture (REDCap) [27,28]. REDCap is a secure, cloud-based software platform designed to support data capture for research studies. A new form will be created for each respondent, and forms will be submitted immediately upon completion of the interview. Paper surveys will be available but will only be used in the event of technology failure. All enumerators will be accompanied by a team leader to ensure accuracy. The survey is in English and will be translated into Dholuo, the most commonly spoken language in this population. Enumerators will use the translation version preferred by the respondent. The intent of each question will be established with enumerators during training prior to administration.

Risk of information sharing will be minimized through a password-protected tablet and mobile platform account with restricted device access. The data will be stored offline on the tablet application until synced to an online, privacy-protected server. All data will be uploaded to the online server for initial analysis through REDCap. Data quality checks will be conducted daily (Fig 3). Feedback from any data discrepancies will be shared with enumerators to maintain high-quality data entry. All variables will be checked line-by-line for any outliers. Surveys will be checked for internal validity, including checking for consistent answers regarding sex and household population. Surveys will also be checked for completeness and any missing data. Discrepancies in data will be resolved by the Data Management Team in conjunction with the enumerators involved. Any changes will be made through REDCap data management tools.

Fig 3

Data quality plan.

Daily, bidirectional data quality checks will be conducted to ensure high-quality data collection. Enumerators will log any quality concerns and any issues during the survey day, which are reported to Team Leaders. Team Leaders review these forms and report to the Supervisor. The Supervisor then creates an aggregate summary that is distributed to the Data Management Team at the end of each survey day. The Data Management Team reviews the data present in REDCap each night for both internal validity and accuracy compared to field reports. This then forms the basis of a quality report that is reviewed with Supervisors and Team Leaders prior to the next survey day.

Raw data will be stored centrally in the REDCap online platform. At the conclusion of each survey round, raw data will be exported to Stata for data cleaning and processing. Data cleaning will be completed by the Data Management Team, and detailed records of all changes will be kept. A final, clean dataset will be kept in a central, password-protected location. All analyses will be conducted using this cleaned dataset.

Data analysis

After administration, data will be exported to the latest version of Stata (StataCorp LP, College Station, TX) for further analysis. Initial analyses will focus on descriptive statistics of health, socioeconomic, and education metrics in the sample population across areas. Further analyses will characterize these metrics in terms of demographic variables and compare these metrics to county and national averages using appropriate statistical tests, including chi-squared tests, t-tests, ANOVA, and non-parametric tests. These analyses will be conducted following each survey in 2021, 2024, and 2027.

Analysis of outcome metrics will be conducted using multivariable linear regression, logistic regression, and generalized estimating equations. Specific primary outcomes of interest include under-five mortality, immunization rate, skilled delivery rate, contraceptive prevalence rate, and antenatal care visits. Longitudinal analyses will be performed at the end of the study in 2027 once multiple timepoints are available. Interim longitudinal analyses will also be performed following each survey administration. Trends in outcomes and potential effects of programs will be assessed. This will involve interrupted time series techniques with a segmented regression to asses intervention effects over repeated observations.

Data availability

The data collected in this research project will be made available after finalization of the study together with corresponding statistical programming code upon request from the Lwala Community Alliance. All data shared will be anonymized.

Discussion

Limitations

Intrinsic to survey research is the use of self-reported data. Some of the survey questions employ direct observation, but the large majority of questions require the participant to recall information and offer sensitive opinions about themselves. We have minimized this limitation to the extent possible by using validated questions that include prompts and training enumerators in making participants comfortable. The cross-sectional nature of the survey makes determining causal relationships difficult. The use of repeated cross-sectional surveys and sophisticated statistical methods will allow some commentary on causation, but the study will not have the same power as a randomized trial. However, randomization is not feasible given the unpredictable nature of expansion wards, which are chosen based on policy, organizational, and financial factors. Further, many of the quasi-experimental methods of program evaluation rely on the parallel trends assumption to create the counter-factual. While this study has a comparison group in the areas in Uriri, there is a single pre-implementation data point available for most intervention areas. This makes evaluation of the parallel trends assumption difficult. Finally, the repeated cross-sectional design loses power by not collecting longitudinal data for the same households over time. However, it was not logistically feasible to visit the same households at each timepoint. Phone numbers will be collected, and further studies will follow smaller sets of outcomes over time within households.

Results dissemination

Data collected in the LHS will be disseminated for use by Lwala internal programming, to inform regional Migori county and national health policy makers, and will be shared widely through presentation at conferences and in the peer-reviewed literature. The reporting will be compliant with the Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement [29].

Conclusion

We describe the proposed methods of the Lwala Household Survey to systematically evaluate public health metrics over time in Migori County, Kenya. This work will be carried out between 2021 and 2027 to inform ongoing programming efforts of the Lwala Community Alliance. Additionally, these results will be useful to regional and national programs and may also be applicable in similar settings outside Kenya. While smaller in size of population covered, the LHS will add to methodological designs and empirical field work of other household surveys, such as the national DHS and regional health and demographic surveillance networks [30,31].

Survey.

Complete survey as it will be administered, although survey has been digitized into a tablet-based program.

(DOCX)

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1 Jul 2021

PONE-D-21-14196

Population-based socio-demographic household assessment of livelihoods and health among communities in Migori County, Kenya over multiple timepoints (2021, 2024, 2027): A study protocol

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Reviewer #1: The proposal for population-based socio-demographic household assessment of livelihoods and health

among communities in Migori County, Kenya over multiple timepoints is clear in its scope and is well-written. The language used is clear, however, it would have been useful if a conceptual diagram/ theory of change for the program was provided for the readers. While the proposal makes it a good case for the proposed study, I do have few questions:

1. The reason for carrying out a study over multiple points in time using repeated cross-sectional methods with control arm while not using a panel data even for few sections [such as child health] of the study was not quite clear. Have authors considered the possibilities of conducting a phone-based follow-up to capture the longitudinal impact of the program on selected outcomes?

2. While authors have mentioned the precautions and social distancing that will be taken during the survey, as a best practice would participants be provided with hand sanitizers/masks during the interview? This is a suggestion given that the survey will be conducted in a disadvantaged community that might not have enough resources and information to protect themselves from COVID-19. I do have similar concern for acquiring participant’s assent/consent by physically receiving participants’ signature or thumbprint. Have authors thought about any other ways of acquiring it [audio-taping]?

3. The survey aims to cover a broad range of indicators using variety of validated tools. I am little unsure about the estimated time [45 minutes] for over 300 questions, some of these being sensitive in nature.

4. It would be important to mention the state of the art for the comparison area. Are there programs similar to the program being implemented in the intervention arm being run by any other non-governmental organization that might impact the interpretation of the difference in the outcomes between intervention and the comparison arm?

5. For mental health outcomes measurement, authors have mentioned about the patient health questionnaire. It would be helpful to mention which version of this tool will be used [PHQ-9- specifically for capturing the depressive symptoms? Etc.].

6. Apart from measuring the community-level impact on the desired outcomes would like to know why does this evaluation lack any component that measures the quality of the service delivery, and the providers’ aspects given that the program specifically entails community health workers, community committees, and high quality facility-based care?

7. As authors mention that the program “serves to promote the health and well-being of communities in the Rongo sub-county”, wouldn’t a mixed-methods approach with focus group discussions combined with the quantitative surveys help understand the community level acceptance, challenges, and expectations from the program?

Reviewer #2: Overall, the proposed study is well-written and technically sound. The repeated cross-sectional design and the objective of capturing key indicators at household, child, and woman-level every three years until 2027 will make the findings from this study very useful for policy implications.

Please see few remarks below that can help improve data collection and quality.

a. A survey tool with 300 questions even with suitable checks and logics in place and covering various domains – interpersonal violence, mental health, IYCF- with so many questions based on recall will be difficult to complete in 45 minutes. The team should re-consider the questionnaire length to reduce the respondent fatigue thereby improving data quality. These are mothers with children <5 years and may not have time to answer ~300 questions.

b. Respondents should be informed and given a time range for example, time to complete interview as 45-60 minutes before starting the survey.

c. Will respondents receive any compensation for their time? They may ask how they benefit from participating in the study.

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Reviewer #1: No

Reviewer #2: No

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29 Jul 2021

We very much appreciate this thoughtful feedback on our submission and are happy to make modifications accordingly. We have copied the reviewers’ and editor’s comments below and have given our responses, including any changes made to the manuscript, below each comment. We have also included both a version with tracked changes and a clean version of our manuscript with this submission.

Editor Comments

This is an important study protocol. A couple of minor suggestions that author should include in the revised version: (i) provide some details on data quality assurance; how exactly it is implemented at various stages starting from tool development, recruitment of investigators to data processing? (ii) Provide a detailed data management plan to illustrate how data flows from field and how it is made ready for analysis.

We agree that readers need detailed information regarding data quality assurance and data management. We have added text to page 15 to help clarify our methods.

Reviewer Comments

Reviewer #1

The proposal for population-based socio-demographic household assessment of livelihoods and health among communities in Migori County, Kenya over multiple timepoints is clear in its scope and is well-written. The language used is clear, however, it would have been useful if a conceptual diagram/ theory of change for the program was provided for the readers.

We agree that a theory of change diagram will be helpful to readers. We have included this new figure and have updated figure numbering as appropriate.

While the proposal makes it a good case for the proposed study, I do have few questions:

1. The reason for carrying out a study over multiple points in time using repeated cross-sectional methods with control arm while not using a panel data even for few sections [such as child health] of the study was not quite clear. Have authors considered the possibilities of conducting a phone-based follow-up to capture the longitudinal impact of the program on selected outcomes?

We agree with the reviewer that longitudinal data for selected outcomes would add additional power, but it was deemed logistically infeasible to visit the same households for each survey. Phone numbers will be collected from participants, which will allow for future longitudinal studies of more focused outcomes. We have added text to the Limitations (page 17, lines 327-331) to clarify this. Further, ongoing data collection by CHWs will allow for longitudinal data collection at the household level that is outside this study.

2. While authors have mentioned the precautions and social distancing that will be taken during the survey, as a best practice would participants be provided with hand sanitizers/masks during the interview? This is a suggestion given that the survey will be conducted in a disadvantaged community that might not have enough resources and information to protect themselves from COVID-19. I do have similar concern for acquiring participant’s assent/consent by physically receiving participants’ signature or thumbprint. Have authors thought about any other ways of acquiring it [audio-taping]?

We share the reviewer’s concerns about safety during COVID-19. Both enumerators and participants are provided with sanitization supplies during the survey. We have added text to page 12-13, lines 222-223 to clarify this. It is interesting to consider other ways of obtaining consent, but our IRB approval only provides for written consent. Given that the first round of surveying has been conducted while this manuscript was under review, we will revisit this possibility during the next timepoint if COVID-19 spread is still a concern.

3. The survey aims to cover a broad range of indicators using variety of validated tools. I am little unsure about the estimated time [45 minutes] for over 300 questions, some of these being sensitive in nature.

We agree that the survey is quite long and covers a broad range of indicators. During our first administration over the past two months, survey times generally ranged from 35-45 minutes. We believe this quick administration was achieved largely due to extensive enumerator training and the use of an electronic tool with programmed skip logic.

4. It would be important to mention the state of the art for the comparison area. Are there programs similar to the program being implemented in the intervention arm being run by any other non-governmental organization that might impact the interpretation of the difference in the outcomes between intervention and the comparison arm?

We agree this is important to clarify for the reader. We have added text to page 6, lines 118-119. There is no organization similar to Lwala operating within the control areas.

5. For mental health outcomes measurement, authors have mentioned about the patient health questionnaire. It would be helpful to mention which version of this tool will be used [PHQ-9- specifically for capturing the depressive symptoms? Etc.].

We have used the PHQ-8, which is the PHQ-9 without the final question regarding suicidal ideation. This question has been omitted due to community concern about the sensitive nature of this question. We have added this to Table 3 to clarify.

6. Apart from measuring the community-level impact on the desired outcomes would like to know why does this evaluation lack any component that measures the quality of the service delivery, and the providers’ aspects given that the program specifically entails community health workers, community committees, and high quality facility-based care?

Although it is not immediately evident from Table 3, the survey does contain questions regarding services provided and satisfaction with services, both for home care provided by CHWs and facility-based care. These questions are within the Programming module. Lwala Community Alliance also has ongoing quality improvement projects at facilities in programming areas that more specifically capture quality metrics.

7. As authors mention that the program “serves to promote the health and well-being of communities in the Rongo sub-county”, wouldn’t a mixed-methods approach with focus group discussions combined with the quantitative surveys help understand the community level acceptance, challenges, and expectations from the program?

We wholeheartedly agree that mixed-methods approaches allow deeper understanding of communities and community health. Our general approach has been to use quantitative data captured in the household survey to inform future mixed-methods projects in concern areas uncovered by the household survey. As data from the household survey is available, these projects will begin.

Reviewer #2

Overall, the proposed study is well-written and technically sound. The repeated cross-sectional design and the objective of capturing key indicators at household, child, and woman-level every three years until 2027 will make the findings from this study very useful for policy implications.

Please see few remarks below that can help improve data collection and quality.

a. A survey tool with 300 questions even with suitable checks and logics in place and covering various domains – interpersonal violence, mental health, IYCF- with so many questions based on recall will be difficult to complete in 45 minutes. The team should re-consider the questionnaire length to reduce the respondent fatigue thereby improving data quality. These are mothers with children <5 years and may not have time to answer ~300 questions.

As stated above, through extensive enumerator trainings and the use of an electronic data capture tool we were able to limit surveys to about 35-45 minutes. We hope to limit questionnaire length for future iterations by limiting questions related to COVID-19, which was a focus of this first survey timepoint.

b. Respondents should be informed and given a time range for example, time to complete interview as 45-60 minutes before starting the survey.

We agree that respondents should understand the length of the survey before beginning. This is included as a part of our consent process.

c. Will respondents receive any compensation for their time? They may ask how they benefit from participating in the study.

Participants will receive 50 KES (~$0.50) in airtime for their participation. Because it is not uncommon for residents within the catchment area to work for $1 per day, we selected 50 KES to avoid coercing potential participants to participate for financial benefit. We have added text to page 11, line 185-186 to clarify this.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

10 Aug 2021

Population-based socio-demographic household assessment of livelihoods and health among communities in Migori County, Kenya over multiple timepoints (2021, 2024, 2027): A study protocol

PONE-D-21-14196R1

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16 Aug 2021

PONE-D-21-14196R1

Population-based socio-demographic household assessment of livelihoods and health among communities in Migori County, Kenya over multiple timepoints (2021, 2024, 2027): A study protocol

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